⚠️ This course is for learning/practice purposes and does not provide an environment for actual deployment. The deployment must comply with the AGPLv3 license.

Class introduction

In this course, AI-based PR code review open source PR-AgentLearn how to install it yourself, create a sample PR to check automatic review results, and set and customize it according to the working environment.

You can also learn Github Workflow, the latest Python development environment settings, Docker Multi-Stage Build, and Project Document Automation (mkDocs), and experience a practical development automation process that can be used in a real team environment rather than a simple tutorial.

Also, you can directly check actual review comments and summary results left by AI on the GitHub Pull Request screen, and experience visible results rather than abstract theories.”

Please be sure to refer to the official documentation for accurate and current information

Official document

GitHub repository: https://github.com/qodo-ai/pr-agent
Official guide: https://qodo-merge-docs.qodo.ai

GitHub PR automation powered by AI

Understand the structure and core logic of PR-Agent and practice it firsthand.

Implement an automated review system by linking GitHub Actions with the OpenAI API.

You can reduce repetitive code review time and create an efficient collaborative environment.

3 reasons this class is special

❶ Practice-oriented AI code review automation

This course doesn't end with “Try installing” open source. It covers ready-to-use customization based on settings, permission management, and workflow optimization experience obtained by operating a pr-agent in a real team environment. It provides a way to balance review quality improvement with review speed, automating labels and comments, and even handling exception cases, so it can be safely introduced into the team repository immediately after taking the course. It also details how to configure a PR checker linked to GitHub Actions, operational tips to avoid conflicts with branch protection rules, and progressive rollout strategies through real examples.

❷ The latest Python & development environment trends

The latest Python version control and packaging are organized around pyproject.toml, and best practices are presented, including virtual environments, linter formatters, and type checker configurations. Learn how to reduce image size and maximize build cache with Docker Multi-Stage Build, and how to implement document automation and theme customization with mkDocs to integrate naturally into the deployment pipeline. Additionally, it covers CLI scaffolding, test automation, and pre-commit hook integrations step by step, and provides practical tips to reduce time to onboard new personnel by documenting team standards.

❸ Docker exercises and simple deployment experience

This course is conducted by directly installing and practicing an AI code review environment using Docker on a local PC. You can deploy a trained container image to Docker Hub, use it to configure a simple pipeline that runs on GitHub Actions, and gain local installation and operation process and deployment experience at the same time. This allows you to understand and practice the entire process from installation, deployment, and pipeline application.

⚖️ License Information

This course uses open source based on the GNU Affero General Public License v3 (AGPLv3) for learning purposes.

Examples provided in the course Explain how to customizeIt's for, Actual distributions are not provided.
Users must Source repositories and official documentationYou must check and install and deploy it yourself.
Personal learning and internal use are free, If the service or distribution is carried out externally, the revised source code must be made public.
The icons and images included in the course materials can be used without any copyright restrictions.

Pull request description enhancement function

AI adds an explanation to the pull request I created.

Pull request reviews

AI analyzes the PR I created and then reviews it.

Pull request code recommendation function

Look at the code in the PR I created and the AI will recommend code to improve.

Pull request question function

You can ask questions and get answers from AI about specific code in PR.

Course effect

Building an AI-based PR automated code review environment
OpenAI · Python · GitHub Actions · Github App · Docker · Mkdocs can be used
Using Project Documentation Automation (MkDocs)
Improve team productivity and code quality at the same time

Recommended target

Those who want to set up an open source PR Agent environment directly in a local environment
Those who need training on the latest Python and Docker trends
Those who want to experience automating documents linked to GitHub Actions
Those who want to bring an AI-based review culture to their team

Notes before taking the course

This course is for those with basic Git experience and Python development knowledge.

It's easier if you know Docker and CI/CD concepts, but we've designed it so that even if you're new to it, you can follow along with step-by-step exercises.

A local PC, GitHub account, and Docker installation are required, and the provided examples and guides complete the AI-based code review environment at the end of the class.

The lessons are based on a sample repository and include checklists and troubleshooting tips for applying to personal projects.

AI-based PR review automation in action: automation starting with PR-agent + modern development environment

AI-based PR review automation in action: automation starting with PR-agent + modern development environment